There are new imaging modalities emerging which provide not just a single image of the patient or the object, but two or more of them in exactly the same geometry.
One example for such a “multi-contrast” imaging modality is spectral computed tomography (CT), where x-ray attenuation due to the photoelectric effect, Compton scattering, and possibly due contrast agents are imaged, thus providing two or three different imaging with different contrasts for different physical effects. Another example is differential phase contrast imaging (DPCI), where the real and imaginary part of the complex refractive index as well as a small angle scattering signal is measured.
Edge detection is an often used task in image segmentation and image visualization. For instance one popular method for segmentation is the so-called region growing algorithm where a region is grown iteratively starting from a seed point until an edge is detected in the image. Another often used method is based on shape model. These models are adapted to the image where a cost function drives the surface of the model to locations with gradients in the image.
It has been found that it is sometimes difficult to extract structural information from these set of images. For instance, in spectral imaging, there is a step of spectral decomposition, where data are decomposed into contribution from different materials or physical effects, e.g. the photo-electric effect and Compton scattering. Decomposition is an ill-posed problem that leads to a strong noise amplification. Having such highly amplified noise impedes accurate segmentation of structures or organs in the material images. Similar problems have been found in DPCI.